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Added 1 hour ago
SM
RQ11673 – Report Developer – Junior
S M Software Solutions Inc
Toronto, ON
- Internship
We are seeking a Junior Report Developer / Data Engineer with experience in developing, implementing, and maintaining enterprise data solutions using modern Azure cloud technologies. The…
Added 1 hour ago
SM
RQ11673 – Report Developer – Junior
S M Software Solutions Inc
Toronto, ON
- Internship
We are seeking a Junior Report Developer / Data Engineer with experience in developing, implementing, and maintaining enterprise data solutions using modern Azure cloud technologies. The…
Added 1 hour ago
SM
RQ11673 – Report Developer – Junior
S M Software Solutions Inc
Toronto, ON
- Internship
We are seeking a Junior Report Developer / Data Engineer with experience in developing, implementing, and maintaining enterprise data solutions using modern Azure cloud technologies. The…
Added 1 hour ago
SM
RQ00245 – Machine Learning Engineer – Senior
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RQ00245 – Machine Learning Engineer – Senior
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RQ00245 – Machine Learning Engineer – Senior
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Vision Talent Co.

AI Engineer - Agentic AI, Commercial Banking

Toronto, ON, Canada

Contract

Apply About employer

Applications open in a new tab.

Job details

Job type: Contract
Apply by: Dec 4, 2026
Experience: 2+ years relevant experience with applied DS/ML foundation and hands-on production retrieval, GenAI or agentic AI delivery
Posting language: English
Posted: Nov 4, 2026

Original job description

Role: AI Engineer – Agentic AI, Commercial Banking

Location: Toronto, hybrid 3 days/week (Tue/Wed anchor)

Contract – 6 months + 1-year extension

FTE conversion possible after ~2 years

Top Must Haves:

2+ years building agentic AI shipped to production (not POCs)
Agent evaluation – offline/online evals, golden datasets, LLM-as-judge, regression gates in CI/CD
Strong Python engineering (FastAPI, Pydantic, pytest, async, APIs)
Applied data science foundation (pandas, NumPy, scikit-learn, SQL, experimentation, error analysis)
RAG and context engineering (hybrid search, reranking, grounding, token budgets)

Required

2+ years relevant experience with applied DS/ML foundation and hands-on production retrieval, GenAI or agentic AI delivery
Strong Python engineering (packaging, typing, testing, async, APIs, logging)
Applied data science: pandas, NumPy, scikit-learn, SQL, statistics, experimentation, error analysis
LLM APIs, prompt engineering, structured outputs, tool calling, RAG or workflow orchestration
Agent architecture: tools, state, memory, routing, planning, retries, fallbacks, HITL
Evaluation dataset design and automated eval pipelines (deterministic checks, rubrics, human eval, calibrated LLM-as-judge)
Enterprise RAG: embeddings, BM25/hybrid, reranking, retrieval-quality evaluation
Git, CI/CD, Docker, cloud deployment, monitoring
Understanding of AI risk and controls
Degree in CS, DS, engineering, stats, math or equivalent experience

Preferred

LangGraph, MCP or comparable orchestration
PyTorch/TensorFlow, gradient boosting, NLP, anomaly detection
LangSmith, Arize Phoenix, MLflow, Azure AI Foundry, Bedrock evals
AWS or Azure AI services, vector search, model gateways
Financial services / Commercial Banking (lending, underwriting, credit, onboarding, RM, treasury, payments)
Regulated-environment production-readiness reviews
Elasticsearch/OpenSearch, Azure AI Search, pgvector; LangChain, LlamaIndex, DeepEval, TruLens

Engagement notes (from Sundar, Oct 30)

Must have 2+ yrs agentic AI shipped to production, not POCs. Banking domain (commercial, retail, treasury, payments, pricing) a plus
FTE conversion possible after ~2 years

About the role

Turn business requirements and solution designs into production-grade Python agents, data-grounded AI capabilities, evaluation pipelines, and observable AI services for Commercial Banking copilots (RM prep, credit analysis, underwriting, pricing). Works with the AI Solution Architect and AI Product Manager and owns the tuning loop from data and context preparation through offline evaluation, online monitoring, user feedback, and continuous improvement.

What you will do

Build production generative/agentic AI apps in Python: agents, tools, workflow graphs, state, memory, APIs, background tasks, human-in-the-loop
Implement approved patterns for orchestration, model/tool integration, data access, security, resiliency, observability
Convert user journeys and acceptance criteria into incremental deliverables and measurable AI behaviours

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More about this job

Responsibilities

Build and deliver production-grade Python agents, data-grounded AI capabilities, evaluation pipelines, and observable AI services for Commercial Banking copilots. Own the continuous improvement cycle from data and context preparation through offline evaluation, online monitoring, user feedback, and tuning.

Requirements

Requires at least two years of relevant experience delivering production retrieval, generative AI, or agentic AI solutions, along with strong Python engineering and an applied data science foundation. Candidates should have experience with agent architecture, RAG, automated evaluation, enterprise deployment and monitoring, and AI risk controls; a relevant degree or equivalent experience is required.

Skills

Agentic AI
Python
Applied Data Science
Agent Evaluation
Retrieval-Augmented Generation
Context Engineering
FastAPI
Pydantic
Pytest
SQL
LLM APIs
Prompt Engineering
Workflow Orchestration
CI/CD
Cloud Deployment
AI Risk Controls

Visa sponsorship

Not detected in the job text

Keywords

Agentic AI
Commercial Banking
Python
FastAPI
Pydantic
Pytest
Pandas
NumPy
Scikit-Learn
SQL
LLM APIs
Prompt Engineering
Tool Calling
Retrieval-Augmented Generation
Hybrid Search
BM25
Reranking
LangGraph
Model Context Protocol
LangSmith
Arize Phoenix
MLflow
Azure AI Foundry
Amazon Bedrock
Docker
CI/CD
Elasticsearch
OpenSearch
Azure AI Search
pgvector

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